1. Technical Field
The present disclosure relates to the segmentation of anatomical structures and more particularly the use and editing of previously incomplete or incorrect segmentation of structures to achieve a correct segmentation.
2. Discussion of Related Art
Medical images, such as computed tomography (CT) and magnetic resonance imaging (MRI) scans, can be segmented to emphasize an object of interest. In a segmentation, pixels are marked as either representing the object of interest or as representing material that lies outside the object of interest. Conventional methods that provide automatic segmentation are not always reliable and can often produce results that include erroneous such markings. Therefore, means of editing the segmentation results (e.g., the pre-segmented image) is necessary. Some conventional editing methods require a user to select foreground and background seed points on the pre-segmented image. The foreground seed points represent points that should have been marked as belonging to the object of interest in the pre-segmented image. The background seed points represent points that should have been marked as lying outside the object of interest in the pre-segmented image. A conventional editing segmentation process, such as one that uses a Graph Cuts or Random Walker method, can then be applied to the seed points and the pre-segmented image to produce a more accurate segmentation.
Since Radiologists are already used to outlining objects of interest in a 3D medical volume with contours, it would be desirable to be able to automatically derive the background and foreground seed points from the drawn contour lines without having a user individually paint them. Thus, there is a need for methods and systems of automatically deriving seed points from contours drawn on an pre-segmentation and editing or correcting the pre-segmentation using the derived seed points.